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| BRIEF RESEARCH ARTICLE |
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| Year : 2022 | Volume
: 66
| Issue : 4 | Page : 504-507 |
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Extent, spectrum, and predictors of cognitive impairment in urban geriatric population in a district of North India
Priya Keshari1, Hari Shankar2
1 Assistant Professor, Department of Family and Community Sciences, Faculty of Science, University of Allahabad, Allahabad, Uttar Pradesh, India
2 Professor, Department of Community Medicine, Institute of Medical Sciences, Banaras Hindu University, Varanasi, Uttar Pradesh, India
| Date of Submission | 11-Jul-2021 |
| Date of Decision | 26-Aug-2022 |
| Date of Acceptance | 21-Oct-2022 |
| Date of Web Publication | 31-Dec-2022 |
Correspondence Address:
Priya Keshari
Department of Family and Community Sciences, Faculty of Science, University of Allahabad, Allahabad, Uttar Pradesh
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/ijph.ijph_1513_21
 Abstract | | |
Cognitive impairment (CI) is no longer considered a normal and inevitable change of aging. This study was carried out to assess extent, spectrum, and predictors of cognitive impairment in the participants. A community-based cross-sectional study was done on 616 urban geriatric participants of Varanasi city selected by multistage sampling procedure. The participants were interviewed about their sociodemographic profile using a predesigned and pretested pro forma, and their cognition was assessed through Mini-Mental State Examination tool. Logistic regression analysis was applied for an inferential purpose. Adjusted odds ratios (AORs) and 95% confidence interval were computed. Extent of cognitive impairment in geriatric participants was 22.4%. AORs were maximum in ≥80 years (21.23; 95% Confidence Interval: 7.05–63.94), in illiterate and just literate participants (13.71; 95% Confidence Interval: 6.49–28.98) and in homemakers (17.0; 95% Confidence Interval: 4.28–67.49). Nine out of 40 urban geriatric participants had cognitive impairment. Adversities of cognitive impairment were more with advancing age, nonengagement in gainful employment, and low literacy levels.
Keywords: Cognition, cognitive impairment, geriatric participants, predictors, urban area
How to cite this article:
Keshari P, Shankar H. Extent, spectrum, and predictors of cognitive impairment in urban geriatric population in a district of North India. Indian J Public Health 2022;66:504-7 |
How to cite this URL:
Keshari P, Shankar H. Extent, spectrum, and predictors of cognitive impairment in urban geriatric population in a district of North India. Indian J Public Health [serial online] 2022 [cited 2023 Feb 4];66:504-7. Available from: https://www.ijph.in/text.asp?2022/66/4/504/366570 |
In the present century, socioeconomic development, public health programs, and the advancement of technology in the field of medical sciences have resulted in the aging of the population. This demographic transition is responsible for increasing burden of degenerative diseases in general and neurodegenerative conditions in particular.[1] Cognition refers to the mental process, in which higher-level functions are carried out by the human brain, including comprehension, use of speech, visual perception and construction, calculation ability, attention, memory, and executive functions. The loss of cognitive abilities is one of the most feared outcomes of aging. Cognitive impairment is no longer considered a normal and inevitable change of aging. There are evidences within[2] and outside India[3] to support that cognitive impairment is an emerging public health problem. A study from India reported a lower prevalence (<10%) of CI in lower socioeconomic status.[4] These studies provided insights regarding emerging threats of cognitive impairment in the reference population. At present, precise treatment for CI is lacking. In fact, changes in cognition often call for prompt and aggressive action. CI not only causes a significant decline in the quality of life of the participants but also put substantial economic burden for their families and society in general. Therefore, early recognition and timely appropriate focused action can pay dividends in reducing the burden of disease. For appropriate care of CI, an early screening of the geriatric population is a cost-effective strategy. Predictors of cognitive impairment are not consistent across geographical boundaries, primarily due to changing sociocultural contexts. Understanding of predictors of CI is desired in the context of planning and execution of focused strategy for reducing its severity and burden. There is a paucity of information on these facets of CI in this region of India. With this background, this study was undertaken in urban Varanasi, Uttar Pradesh, to estimate the extent and spectrum of cognitive impairment in the study participants and to assess the predictors of cognitive impairment in the participants.
This study was carried out in nine census-enumerated wards of Varanasi city. As per the pilot study in the nonstudy area, cognitive impairment in geriatric participants was 36.7%. For estimation of sample size, the prevalence of cognitive impairment obtained in the pilot study was rounded to 40%. Taking this prevalence, 5% permissible error (absolute), design effect of 1.5, and nonresponse rate of 10%, the estimated sample size was 616. The selection of the participants was made through multistage sampling procedure. In the first stage, the sampling frame comprised 90 census enumeration wards of Varanasi city. In order to have a representative sample of wards, 10% (i.e., nine) of wards were selected by simple random sampling. In the second stage, from the selected wards, households were selected by systematic random sampling. This was followed by the selection of families and one study participant from the family by lottery method. If there was no geriatric participant in the family, one was chosen from adjoining family using the same procedure.
Participants who gave their consent for the study were included in the study. Participants with terminal illness, having serious mental abnormality, and/or duration of stay in the study area <6 months were not included in the sampling frame, and thus, they were excluded from the study.
After obtaining consent, participants were interviewed through a predesigned and pretested proforma to obtain information about sociodemographic characteristics. In order to compute socioeconomic status as per the Kuppuswamy Classification, education and occupation of the head of the family as well as the total family income were assessed through an interview technique using the above-mentioned tool. As per this classification, participants were categorized into upper, upper-middle, lower-middle, upper-lower, and lower classes.[5]
Extent and spectrum of cognitive impairment in geriatric participants was assessed by interviewing them using Mini-Mental State Examination (MMSE).[6] Based on the MMSE score, literate participants were categorized as without CI (24–30), mild CI (18–23), and severe CI (<18). Two items (namely, read and obey the following and write a sentence) having score of one each were not administrable for illiterate participants; therefore, the maximum possible score and cutoff values were reduced by 2 points; thus, participants with a score 22–28, 16–21, and <16 were categorized as without CI, mild CI, and severe CI, respectively. Approval of the study protocol was obtained by the Ethical Committee of Banaras Hindu University, India.
The data were analyzed using SPSS software (SPPS version 22.0 IBM Corp., Armonk, NY). Descriptive statistics were used to present categorical data, namely, proportion, mean, and standard deviation. Univariate analysis was performed for associates of cognitive impairment, and significant variables (P < 0.05) were put in the logistic regression model. Adjusted odds ratio (AOR) and 95% confidence interval were computed to pinpoint the predictors of cognitive impairment.
Of 616 participants, 20.9% (95% confidence interval: 17.7%–24.1%) of participants had mild CI, whereas 1.5% (95% confidence interval: 0.5%–2.5%) of participants had severe cognitive impairment. Average scores for orientation of time, orientation of place, registration, attention and calculation, recall, and language were 81.4%, 85.8%, 100.0%, 79.0%, 96.6%, and 82.5% of the maximum score respective categories. Of 616 participants, in the case of 252 (40.9%) illiteracy, test item was not administered for “read and obey” and “write a sentence.” Of 364 participants, 284 (78.0%) were able to read correctly, and “write a sentence” was correctly done by 278 (76.4%) participants. “Copy the design” item was performed by both literate and illiterate participants; 63.1% were able to do this activity correctly.
Significant association of age, gender, marital status, education, occupation, and socioeconomic status of the participants and their cognitive status was noted. Extent of cognitive impairment (CI) was 13.0% in 60–69 years, 32.9% in 70–79 years, and 58.8% in >80 years age. As much as 6.5% of male and 35.6% of female participants were with CI. Cognitive impairment was 18.1% in married, 8.8% in widower, and 45.7% in widowed participants. CI was maximum (40.5%) in illiterate plus just literate participants, whereas none of the participants had literacy status as graduate and above had CI. Extent of cognitive impairment was 2.3% in self-employed plus service plus retired, 2.1% in skilled and unskilled workers, and 14.5% in unemployed participants. This was maximum (41.1%) in homemakers. Cognitive impairment was maximum in the participants belonging to upper-lower and lower on the basis of the Kuppuswamy classification. Of 258 participants belonging to upper-lower and lower class, 68 (26.3%) had CI. This was 4.8% in upper, 17.9% in upper-middle, and 25% in lower-middle class. Religion, caste, type, and size of the family were not significantly (P > 0.05) associated with cognition status of the study participants. In total, 391 (76.8%) Hindu and 87 (81.3%) Muslim participants were without CI; participants without CI were maximum (82.5%) for other caste categories and least (74.5%) for the SC/ST participants. As much as 77.9%, 79.7%, and 75.6% of participants from nuclear, joint, and three-generation families, respectively, were without CI. Participants without CI having a family size of <3, 3–6, and >6 were 83.8%, 73.8%, and 78.8%, respectively [Table 1]. | Table 1: Association of sociodemographic variables with cognitive impairment in the participants
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Age, education, and occupation were identified as predictors of cognitive impairment in the logistic model. AOR for cognitive impairment was 21.23 (95% confidence interval: 7.05–63.95) for ≥80 years' and 4.58 (95% confidence interval: 2.42–8.69) for 70–79 years' age group. In comparison to participants having an educational level as primary and above, AOR for cognitive impairment was 13.71 (95% confidence interval: 6.49–26.98) for illiterate and just literate participants. The risk of cognitive impairment was significantly more (P < 0.05) for unemployed participants and homemakers; AOR for CI in the later categories was 7.18 (95% confidence interval: 1.39–36.97) and 17.0 (95% confidence interval: 4.28–67.19), respectively [Table 2].
Cognitive impairment has been identified as a significant health problem in urban geriatric participants in this study. A study conducted in Spain has reported a lower prevalence (1 out 10) of CI.[7] In contrast to this, a study in community-dwelling elderly population in Turkey reported a higher (1 out of 4) CI.[3] Some studies conducted in India have reported a lower prevalence of CI.[8],[9] In contrast to this, a higher figure of CI was reported in a study in Hyderabad.[2] Variation in the prevalence of cognitive impairment may be influenced by the social support extended to geriatric participants and prevailing sociocultural milieu. It has been observed in normally aging older adults of urban Lucknow, these participants had objective cognitive dysfunction in the areas of orientation, concentration, and functioning/self-care.[9]
As per the logistic model, advancing age, low literacy levels, and nonengagement in gainful employment have been identified as significant predictors of CI. There are evidence recognizing advancing age,[8],[10] low literacy,[8],[10] and nonengagement of gainful employment as the risk of CI.[8] Lower socioeconomic status and poverty exert significant influence on the cognition of geriatric participants. This has been demonstrated by several workers from India.[2],[8] Besides highlighting the problem of CI in urban geriatric participants, predictors of CI identified in this study provide significant inputs for focused action to tackle the burden of cognitive impairment, thereby improving their quality of life.
Nine out of 40 urban geriatric participants had cognitive impairment. Higher risk of CI prevailed with advancing age, low literacy levels, and in participants not engaged in gainful employment.
Acknowledgment
The authors would like to thank all the participants for their participation in the study.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | |  |
| 1. | United Nations Population Fund (UNFPA) and Help Age International. Ageing in the Twenty-first Century a Celebration and Challenge Executive Summary. New York: UNFPA and London, Help Age International; 2012. p. 3-4.  |
| 2. | Rao SS, Chennamsetty SK, Kuna SR. A cross sectional study of cognitive impairment and morbidity profile of inmates of old age home. Sch J App Med Sci 2014;2:1506-13. |
| 3. | Arguvanli S, Akin S, Deniz Safak E, Mucuk S, Öztürk A, Mazicioğlu MM, et al. Prevalence of cognitive impairment and related risk factors in community-dwelling elderly in Kayseri, Turkey. Turk J Med Sci 2015;45:1167-72. |
| 4. | Kumar S, Tiwari SC, Tripathi RK, Pandey NM. Socio economic status has bearing on the mini mental state examination score among. Indian urban elderly. J Geriatr Ment Health 2014;1:90-3. [Full text] |
| 5. | Kohli C, Kishore J, Kumar N. Kuppuswamy's socioeconomic scale-update for July 2015. Int J Preven Curat Comm Med 2015;1:26-8. |
| 6. | Kurlowicz L, Wallace M. The mini-mental state examination (MMSE). J Gerontol Nurs 1999;25:8-9. |
| 7. | Lara E, Koyanagi A, Olaya B, Lobo A, Miret M, Tyrovolas S, et al. Mild cognitive impairment in a Spanish representative sample: Prevalence and associated factors. Int J Geriatr Psychiatry 2016;31:858-67. |
| 8. | Sengupta P, Benjamin AI. Prevalence of depression and associated risk factors among the elderly in urban and rural field practice areas of a tertiary care institution in Ludhiana. Indian J Public Health 2015;59:3-8. [ PUBMED] [Full text] |
| 9. | Tripathi RK, Tiwari SC. Cognitive dysfunction in normally aging urban older adults: A community-based study. Indian J Psychol Med 2011;33:177-81. [ PUBMED] [Full text] |
| 10. | Giri M, Chen T, Yu W, Lü Y. Prevalence and correlates of cognitive impairment and depression among elderly people in the world's fastest growing city, Chongqing, People's Republic of China. Clin Interv Aging 2016;11:1091-8. |
[Table 1], [Table 2]
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